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BUG: formatting for Series was printing multiple Dtype lines for long display #2930

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Merged
merged 1 commit into from
Feb 26, 2013
Merged

BUG: formatting for Series was printing multiple Dtype lines for long display #2930

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6 changes: 3 additions & 3 deletions pandas/core/format.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,14 +65,14 @@

class SeriesFormatter(object):

def __init__(self, series, buf=None, header=True, length=True,
na_rep='NaN', name=False, float_format=None, dtype=True):
def __init__(self, series, buf=None, header=True, length=True, dtype=True,
na_rep='NaN', name=False, float_format=None):
self.series = series
self.buf = buf if buf is not None else StringIO(u"")
self.name = name
self.dtype = dtype
self.na_rep = na_rep
self.length = length
self.dtype = dtype
self.header = header

if float_format is None:
Expand Down
21 changes: 13 additions & 8 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1047,9 +1047,10 @@ def __unicode__(self):
elif len(self.index) > 0:
result = self._get_repr(print_header=True,
length=len(self) > 50,
name=True)
name=True,
dtype=True)
else:
result = u'Series([], dtype=%s)' % self.dtype
result = u'Series([], Dtype: %s)' % self.dtype

assert type(result) == unicode
return result
Expand All @@ -1069,9 +1070,10 @@ def _tidy_repr(self, max_vals=20):
"""
num = max_vals // 2
head = self[:num]._get_repr(print_header=True, length=False,
name=False)
dtype=False, name=False)
tail = self[-(max_vals - num):]._get_repr(print_header=False,
length=False,
dtype=False,
name=False)
result = head + '\n...\n' + tail
result = '%s\n%s' % (result, self._repr_footer())
Expand All @@ -1085,7 +1087,7 @@ def _repr_footer(self):
com.pprint_thing(self.dtype.name))

def to_string(self, buf=None, na_rep='NaN', float_format=None,
nanRep=None, length=False, name=False):
nanRep=None, length=False, dtype=False, name=False):
"""
Render a string representation of the Series

Expand All @@ -1100,6 +1102,8 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None,
default None
length : boolean, default False
Add the Series length
dtype : boolean, default False
Add the Series dtype
name : boolean, default False
Add the Series name (which may be None)

Expand All @@ -1114,7 +1118,7 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None,
na_rep = nanRep

the_repr = self._get_repr(float_format=float_format, na_rep=na_rep,
length=length, name=name)
length=length, dtype=dtype, name=name)

assert type(the_repr) == unicode

Expand All @@ -1123,15 +1127,15 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None,
else:
print >> buf, the_repr

def _get_repr(self, name=False, print_header=False, length=True,
def _get_repr(self, name=False, print_header=False, length=True, dtype=True,
na_rep='NaN', float_format=None):
"""

Internal function, should always return unicode string
"""

formatter = fmt.SeriesFormatter(self, name=name, header=print_header,
length=length, na_rep=na_rep,
length=length, dtype=dtype, na_rep=na_rep,
float_format=float_format)
result = formatter.to_string()
assert type(result) == unicode
Expand Down Expand Up @@ -3287,7 +3291,8 @@ def _repr_footer(self):

namestr = "Name: %s, " % str(
self.name) if self.name is not None else ""
return '%s%sLength: %d' % (freqstr, namestr, len(self))
return '%s%sLength: %d, Dtype: %s' % (freqstr, namestr, len(self),
com.pprint_thing(self.dtype.name))

def to_timestamp(self, freq=None, how='start', copy=True):
"""
Expand Down
27 changes: 15 additions & 12 deletions pandas/tests/test_format.py
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Original file line number Diff line number Diff line change
Expand Up @@ -585,6 +585,15 @@ def test_wide_repr_wide_long_columns(self):
self.assertTrue('ccccc' in result)
self.assertTrue('ddddd' in result)

def test_long_series(self):
n = 1000
s = Series(np.random.randint(-50,50,n),index=['s%04d' % x for x in xrange(n)], dtype='int64')

import re
str_rep = str(s)
nmatches = len(re.findall('Dtype',str_rep))
self.assert_(nmatches == 1)

def test_to_string(self):
from pandas import read_table
import re
Expand Down Expand Up @@ -1119,7 +1128,7 @@ def test_to_string(self):
format = '%.4f'.__mod__
result = self.ts.to_string(float_format=format)
result = [x.split()[1] for x in result.split('\n')]
expected = [format(x) for x in self.ts] + [u'float64']
expected = [format(x) for x in self.ts]
self.assertEqual(result, expected)

# empty string
Expand All @@ -1132,7 +1141,7 @@ def test_to_string(self):
# name and length
cp = self.ts.copy()
cp.name = 'foo'
result = cp.to_string(length=True, name=True)
result = cp.to_string(length=True, name=True, dtype=True)
last_line = result.split('\n')[-1].strip()
self.assertEqual(last_line, "Freq: B, Name: foo, Length: %d, Dtype: float64" % len(cp))

Expand All @@ -1149,8 +1158,7 @@ def test_to_string_mixed(self):
expected = (u'0 foo\n'
u'1 NaN\n'
u'2 -1.23\n'
u'3 4.56\n'
u'Dtype: object')
u'3 4.56')
self.assertEqual(result, expected)

# but don't count NAs as floats
Expand All @@ -1159,17 +1167,15 @@ def test_to_string_mixed(self):
expected = (u'0 foo\n'
'1 NaN\n'
'2 bar\n'
'3 baz\n'
u'Dtype: object')
'3 baz')
self.assertEqual(result, expected)

s = Series(['foo', 5, 'bar', 'baz'])
result = s.to_string()
expected = (u'0 foo\n'
'1 5\n'
'2 bar\n'
'3 baz\n'
u'Dtype: object')
'3 baz')
self.assertEqual(result, expected)

def test_to_string_float_na_spacing(self):
Expand All @@ -1181,8 +1187,7 @@ def test_to_string_float_na_spacing(self):
'1 1.5678\n'
'2 NaN\n'
'3 -3.0000\n'
'4 NaN\n'
u'Dtype: float64')
'4 NaN')
self.assertEqual(result, expected)

def test_unicode_name_in_footer(self):
Expand Down Expand Up @@ -1216,14 +1221,12 @@ def test_timedelta64(self):
result = y.to_string()
self.assertTrue('1 days, 00:00:00' in result)
self.assertTrue('NaT' in result)
self.assertTrue('timedelta64[ns]' in result)

# with frac seconds
s = Series(date_range('2012-1-1', periods=3, freq='D'))
y = s-datetime(2012,1,1,microsecond=150)
result = y.to_string()
self.assertTrue('00:00:00.000150' in result)
self.assertTrue('timedelta64[ns]' in result)

def test_mixed_datetime64(self):
df = DataFrame({'A': [1, 2],
Expand Down